Over the years, data analytics has pervaded almost all sectors. Startups, SMEs, and large organizations are increasingly using analytics to cut costs, strengthen their marketing campaigns, enhance customer experience through personalization, and boost business overall.
Today, data analytics is faster, more accurate, and even more cost-effective, and at least some of the process is fully automated thanks to new technologies like artificial intelligence and blockchain. This then begs the question – Where are data analytics trends headed in 2022? What are the predictions in data analytics trends for the new year?
Our team of data scientists has studied the patterns, and this is what they forecast for the new year (in no order of priority):
1. Cloud Analytics Will Get Even More Traction
Cloud services are gaining popularity in organizations worldwide. Cloud analytics is the key to quickly and accurately delivering the desired results to users. In fact, cloud technologies are becoming the new normal, as seen in 2021.
So why is cloud analytics considered to be the future of cloud computing?
Here are the main reasons:
– It reduces the cost of computing
– It provides various computing resources for data analysis
– It requires less power
– It is easily scalable
– It helps in user experience
Cloud analytics can help enterprises reduce the cost of managing their IT infrastructure. It helps them identify what data is valuable and what is not. The real-time intelligence delivered to the company can help them improve the user experience of their application by optimizing the performance and increasing the ROI (Return on Investment).
Here are some more reasons why cloud analytics is the latest buzzword in the analytics industry. Cloud analytics builds on the typical use of cloud-based applications to deliver faster, more efficient, and cheaper data analysis solutions. Another reason is that cloud analytics solves a significant problem in the data management industry that is not being solved.
Organizations around the world are struggling to manage the vast amount of data that exists in their environments. This is a lot harder than it sounds, especially for companies that aren't making enough money to go down and hire armies of data scientists or data analysts.
Cloud also offers enterprise scalability for faster time to value than traditional on-premises analytic platforms. The ability to run analytical models on fast and highly available servers across the globe, making analytics processing at the edge possible.
Cloud computing providers such as AWS continuously update their infrastructure with the latest technology, and users benefit from those updates immediately without having to perform any maintenance themselves.
It takes extensive time from IT professionals to build and maintain on-premises infrastructure, time that could otherwise be spent on projects that improve the business. That's what makes the cloud an attractive proposition today.
2. Unstructured Data Shall Share The Podium With Structured Data
Like cloud analytics, this one is a given. All this while, businesses, because of the race to collect as much data as possible about their consumers and clients, focused primarily on structured data. It was easy to collect and easy to compartmentalize; everything was neat. Much of the time, though, what was ignored was a valuable piece of information loosely dubbed "unstructured data".
Here's what you need to know about both types of data and why businesses are shifting toward unstructured data:
Structured data is precisely what it sounds like: data that has been organized in a way that makes it easier to use. Examples of structured data can include address information, contact information, product or service descriptions, and content from websites. It is typically organized into tables and includes standard tags to identify fields within the table. It can also be text, numbers, or other structured formats. Because of its structure, it's always easier to key structured data into databases.
Unstructured data obviously has no form at all, and it does not follow a set system of organization. Examples of unstructured data include pictures, videos, audio recordings, and even text. This can contain inconsistent information, lack standard tags, or fail to follow a specific pattern. With the exploding volume of digital data today, it's easy to forget that all data was once unstructured.
There are many ways to build a data model for structured or unstructured data. No matter which one you choose, the goal is to make your content easy to work with and to extract information that can be analyzed in various ways.
Structured data is any data that can be organized into tables, list boxes, lists, and other types of data structures. This is true for all kinds of data, whether it's XML, HTML, XHTML, or CSV (comma-separated values). It's simply data that can be entered into fields in a database. The only difference is the type of data. For example, image data and sound data are both structured, but they're not the same.
Because much of the data that flows in today is unstructured, it does not end up in databases, leaving companies bereft of its benefits. But with new privacy laws coming into effect and Google planning to phase out 3rd-party cookies, businesses are being compelled to look at more and more unstructured data. By 2022, it will be crucial to develop these skills, which require learning new capabilities for unstructured data analytics and management.
3. Data Fabric Will Ease Data Management
In the middle of 2020, Express Analytics published an article on the growing importance of data fabric in data analytics. The cloud industry is constantly evolving, but when you look at data fabric, it's nearly impossible to believe that a concept like this was once just theoretical.
What is a Data Fabric?
It's the term used to describe a bridge between data centers that enables faster network connectivity. Data fabric is a setup that helps organizations make better use of their data. The benefits of a data fabric include self-service data consumption, embedded governance, and automated data integration. Organizations can gain faster insights from their data with a data fabric. Additionally, it reduces data inconsistency and compliance risks and improves data quality.
Data fabric describes the physical topology between servers and other hardware components that process and store data. The idea is that data can be moved between different components, such as physical servers, virtual machines, and storage accounts. As we said earlier, it's a concept that's been around for a while, and it's still considered cutting-edge.
A hybrid data fabric also exists to connect two or more physical networks. This type of data fabric provides a logical point of connectivity to switch traffic between physical networks.
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How Data Fabric Relates to Modern Enterprise Architecture
The physical world is one of the biggest challenges in software-defined networking, and much of that difficulty stems from the need to connect switches to provide network connectivity and effectively route traffic.
Using a data fabric reduces the amount of management required and enables centralized management of resources and settings across multiple physical and virtual resources.
Think about a large, hypothetical fabric spanning multiple locations, including the cloud, that connects all types of structured and unstructured data, along with methods to access and analyze it. Data fabrics are unlike real-world fabrics because they have no fixed shape, are scalable, and are built with flexibility that accounts for data processing, management, and storage. Teams, both internal and external to the enterprise, can use it for a variety of analytical and operational needs.
Organizations can easily adapt their infrastructure to changing technology needs using a data fabric. A data fabric enables a consolidated, unified data management framework by simplifying the connection of various infrastructure endpoints. Organizers don't have to worry about the precise location of data.
4. Data Mesh Provides New Architecture Alternative
Businesses today have several options for building a data architecture that meets their needs. Thanks to newer technology, they can now choose, for example, between data fabrics and data meshes.
The data mesh approach for managing analytical data is based on a modern, distributed architecture. Because of data mesh, end users can access and query data wherever it lives without first transporting it to a data lake or data warehouse, meaning they can do it right on the edge.
In the decentralized data mesh model, domain-specific teams manage, own, and serve the data as a product. The main goal of data mesh is to eliminate data availability and accessibility issues at scale. A data mesh allows businesses to access, analyze, and operationalize insights from any data source, in any location, without involving experts.
By definition, data meshes make data accessible, readily available, discoverable, secure, and interoperable. Faster access to query data directly translates to a quicker time to value without transporting data.
On the surface, there seem to be similarities between data fabric and data mesh. But the two are fundamentally different.
Data mesh is a new software approach that, instead of central databases, uses point-to-point connections to simplify the architecture. This means a business saves on hardware costs. What it also means is that data is readily available, easily found, and interoperable with the apps that need access to such data.
Data mesh is a service that helps us sync data across devices. Basically, it synchronizes data across different apps, databases, and local devices that are linked via the same protocol (HTTP or HTTPS) using a push model. The service helps synchronize devices connected to the same account. This is especially useful when you want to keep your data current on all of your devices. It can be used for software updates, maintaining files synchronized, or moving them from one location to another.
By implementing a single data fabric that spans many data sources, a business can provide unified management for all – data scientists, data stewards, and end users. One must remember here that the management of storage remains distributed, not the storage itself.
Simply put, a data mesh helps distributed teams manage data as they want, with overlapping governance guidelines. A mesh also forces management to think of a new approach to data management architectures.
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A data fabric, on the other hand, aims to build a single layer that spans all that distributed data.
5. Automated Machine Learning Will Take Over Tedious Tasks
Advances in artificial intelligence have enabled the development of automated machine learning (ML) software that helps data analysts perform a range of tasks. Many of these systems have proved very effective at performing the tasks they were built for, across fields such as bioinformatics, imaging, and natural language processing (NLP).
Automatic machine learning, or AutoML, automates the tedious, iterative process of developing models. With it, data scientists, analysts, and developers can build ML models at scale, with efficiency, and with consistent quality. One of the primary reasons for this tech's growth is that it will significantly reduce labor costs. Automation can reduce the time and effort required to build and optimize models, saving time and money.
ML has been widely implemented in real-world applications. Some of the earliest applications of ML, including rule-based learning and prediction and verification in insurance, have evolved into systems used in real-world settings. ML can be applied to a wide range of areas, including literature research and information retrieval, with direct financial implications. ML and AI algorithms have been used in the development of virtual assistants.
The development of ML models has increased the possibilities for complex decision-making in finance and other areas, including financial arbitrage.
With the advent of Auto ML tools, everyday tasks like hyper-parameter tuning, model selection, experiment design, and API support will get automated. Developers and data scientists will then be able to focus on what matters most: their models and applications, rather than wasting time and energy managing low-level infrastructure.
